{
 "cells": [
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     "text": [
      "2024-05-12 09:54:52.746753: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-05-12 09:54:52.746874: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-05-12 09:54:52.868874: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start on cuda:0 device.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from PIL import Image\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from torchvision import models, transforms\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "\n",
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"start on {} device.\".format(device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab2145a5",
   "metadata": {
    "execution": {
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   "source": [
    "# 保存训练模型名称\n",
    "save_model_name = \"vgg16_base_cifar10\"\n",
    "# 训练的轮数\n",
    "epoch = 50\n",
    "# 一批数据大小\n",
    "batch_size = 128\n",
    "# 丢弃率\n",
    "dropout = 0.5\n",
    "# 数据集路径\n",
    "# /kaggle/input/dogs-vs-cats\n",
    "dataset_path = \"./dataset\"\n",
    "# tensorboard路径\n",
    "writer = SummaryWriter(\"./logs\")\n",
    "# 学习率\n",
    "# learning_rate = 0.01\n",
    "# 1e-2=1 x (10)^(-2) = 1 /100 = 0.01\n",
    "learning_rate = 1e-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38a81d95",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./dataset/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 170498071/170498071 [00:06<00:00, 25797864.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./dataset/cifar-10-python.tar.gz to ./dataset\n",
      "Files already downloaded and verified\n",
      "训练数据集长度: 50000\n",
      "测试数据集长度: 10000\n",
      "torch.Size([128, 3, 32, 32])\n",
      "torch.Size([128, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "transform = transforms.ToTensor()\n",
    "train_data = torchvision.datasets.CIFAR10(dataset_path, train=True, download=True,\n",
    "                                          transform=transform)\n",
    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=0, drop_last=False)\n",
    "\n",
    "test_data = torchvision.datasets.CIFAR10(dataset_path, train=False, download=True,\n",
    "                                         transform=transform)\n",
    "test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=0, drop_last=False)\n",
    "\n",
    "train_data_size = len(train_data)\n",
    "test_data_size = len(test_data)\n",
    "print(\"训练数据集长度: {}\".format(train_data_size))\n",
    "print(\"测试数据集长度: {}\".format(test_data_size))\n",
    "\n",
    "for data in train_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break\n",
    "\n",
    "for data in test_dataloader:\n",
    "    imgs, targets = data\n",
    "    print(imgs.shape)\n",
    "    break"
   ]
  },
  {
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   "source": [
    "class VGG16_BASE(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(VGG16_BASE, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(256),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(512),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "            # vgg16原本时 nn.AdaptiveAvgPool2d((7, 7))，由于输入图片大小导致\n",
    "            # nn.AvgPool2d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            # 原始模型vgg16输入image大小是224 x 224\n",
    "            # CIFAR10输入image大小是32 x 32\n",
    "            # 大小是小了 7 x 7倍\n",
    "            nn.Linear(512, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 10),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.features(x)\n",
    "        # output = output.view(output.size(0), -1)\n",
    "        output = torch.flatten(output, 1)\n",
    "        output = self.classifier(output)\n",
    "        return output"
   ]
  },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG16_BASE(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (12): ReLU(inplace=True)\n",
      "    (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (16): ReLU(inplace=True)\n",
      "    (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (19): ReLU(inplace=True)\n",
      "    (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (26): ReLU(inplace=True)\n",
      "    (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (32): ReLU(inplace=True)\n",
      "    (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (36): ReLU(inplace=True)\n",
      "    (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (39): ReLU(inplace=True)\n",
      "    (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (42): ReLU(inplace=True)\n",
      "    (43): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=512, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=4096, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 网络模型相关初始化\n",
    "# 创建网络模型\n",
    "mymod = VGG16_BASE()\n",
    "print(mymod)\n",
    "\n",
    "mymod = mymod.to(device)\n",
    "# 损失函数\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "# loss_fn = loss_fn.to(device)\n",
    "\n",
    "# 优化器\n",
    "optimizer = torch.optim.SGD(mymod.parameters(), lr=learning_rate)\n",
    "# 学习率衰减，每10轮减少90%\n",
    "scheduler = StepLR(optimizer, step_size=10, gamma=0.1) "
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start train.\n",
      "第0轮训练用时：14.834582090377808 s\n",
      "第0轮训练loss：1.4686641994857788\n",
      "第0轮训练精度：0.46118\n",
      "------\n",
      "第0轮测试用时：2.3389055728912354 s\n",
      "第0轮测试loss：1.9660660730361939\n",
      "第0轮测试精度：0.3972\n",
      "------\n",
      "第1轮训练用时：13.877942562103271 s\n",
      "第1轮训练loss：0.8620192091369628\n",
      "第1轮训练精度：0.7024\n",
      "------\n",
      "第1轮测试用时：2.3580915927886963 s\n",
      "第1轮测试loss：0.9911450500488281\n",
      "第1轮测试精度：0.6607\n",
      "------\n",
      "第2轮训练用时：13.847756385803223 s\n",
      "第2轮训练loss：0.6173631027030945\n",
      "第2轮训练精度：0.7911\n",
      "------\n",
      "第2轮测试用时：2.3195271492004395 s\n",
      "第2轮测试loss：1.0543726905822755\n",
      "第2轮测试精度：0.6647\n",
      "------\n",
      "第3轮训练用时：13.881203889846802 s\n",
      "第3轮训练loss：0.4840839704322815\n",
      "第3轮训练精度：0.83696\n",
      "------\n",
      "第3轮测试用时：2.2828011512756348 s\n",
      "第3轮测试loss：0.7477403902053833\n",
      "第3轮测试精度：0.7539\n",
      "------\n",
      "第4轮训练用时：13.847419738769531 s\n",
      "第4轮训练loss：0.3867661487483978\n",
      "第4轮训练精度：0.86928\n",
      "------\n",
      "第4轮测试用时：2.2726049423217773 s\n",
      "第4轮测试loss：0.6754068071365357\n",
      "第4轮测试精度：0.7893\n",
      "------\n",
      "第5轮训练用时：13.899868965148926 s\n",
      "第5轮训练loss：0.2978483360862732\n",
      "第5轮训练精度：0.89914\n",
      "------\n",
      "第5轮测试用时：2.3416740894317627 s\n",
      "第5轮测试loss：0.9354587221145629\n",
      "第5轮测试精度：0.7256\n",
      "------\n",
      "第6轮训练用时：13.839108943939209 s\n",
      "第6轮训练loss：0.23911016478538513\n",
      "第6轮训练精度：0.91928\n",
      "------\n",
      "第6轮测试用时：2.285907030105591 s\n",
      "第6轮测试loss：0.849236897277832\n",
      "第6轮测试精度：0.7589\n",
      "------\n",
      "第7轮训练用时：13.881641864776611 s\n",
      "第7轮训练loss：0.18376528284549712\n",
      "第7轮训练精度：0.93688\n",
      "------\n",
      "第7轮测试用时：2.282712697982788 s\n",
      "第7轮测试loss：0.7829856100082397\n",
      "第7轮测试精度：0.7862\n",
      "------\n",
      "第8轮训练用时：13.828070402145386 s\n",
      "第8轮训练loss：0.15267847972869872\n",
      "第8轮训练精度：0.94996\n",
      "------\n",
      "第8轮测试用时：2.2843856811523438 s\n",
      "第8轮测试loss：0.9017368837356567\n",
      "第8轮测试精度：0.7713\n",
      "------\n",
      "第9轮训练用时：13.883497953414917 s\n",
      "第9轮训练loss：0.11204219640254974\n",
      "第9轮训练精度：0.9625\n",
      "------\n",
      "第9轮测试用时：2.270786762237549 s\n",
      "第9轮测试loss：0.6597767189979553\n",
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      "------\n",
      "第10轮训练用时：13.848625898361206 s\n",
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      "第10轮训练精度：0.98966\n",
      "------\n",
      "第10轮测试用时：2.2889606952667236 s\n",
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      "------\n",
      "第11轮训练用时：13.859564304351807 s\n",
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      "------\n",
      "第11轮测试用时：2.289249897003174 s\n",
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      "------\n",
      "第12轮训练用时：13.840794801712036 s\n",
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      "------\n",
      "第12轮测试用时：2.2637484073638916 s\n",
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      "------\n",
      "第13轮训练用时：13.884028434753418 s\n",
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      "------\n",
      "第13轮测试用时：2.289475202560425 s\n",
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      "------\n",
      "第14轮训练用时：13.847674131393433 s\n",
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      "------\n",
      "第14轮测试用时：2.2938616275787354 s\n",
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      "------\n",
      "第15轮训练用时：13.892963886260986 s\n",
      "第15轮训练loss：0.0034242189694941045\n",
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      "------\n",
      "第15轮测试用时：2.320808172225952 s\n",
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      "------\n",
      "第16轮训练用时：13.849454879760742 s\n",
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      "------\n",
      "第16轮测试用时：2.295596122741699 s\n",
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      "------\n",
      "第17轮训练用时：13.906275272369385 s\n",
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      "------\n",
      "第17轮测试用时：2.2788734436035156 s\n",
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      "------\n",
      "第18轮训练用时：13.839287042617798 s\n",
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      "------\n",
      "第18轮测试用时：2.297456979751587 s\n",
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      "------\n",
      "第19轮训练用时：13.869282960891724 s\n",
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      "------\n",
      "第19轮测试用时：2.275031089782715 s\n",
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      "------\n",
      "第20轮训练用时：13.859020471572876 s\n",
      "第20轮训练loss：0.0014978798875585199\n",
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      "------\n",
      "第20轮测试用时：2.269212245941162 s\n",
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      "------\n",
      "第21轮训练用时：13.866504669189453 s\n",
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      "第21轮训练精度：0.99984\n",
      "------\n",
      "第21轮测试用时：2.2963545322418213 s\n",
      "第21轮测试loss：0.6716607925415039\n",
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      "------\n",
      "第22轮训练用时：13.84445571899414 s\n",
      "第22轮训练loss：0.001371608820129186\n",
      "第22轮训练精度：0.99988\n",
      "------\n",
      "第22轮测试用时：2.273073434829712 s\n",
      "第22轮测试loss：0.6701409139633179\n",
      "第22轮测试精度：0.8682\n",
      "------\n",
      "第23轮训练用时：13.856121063232422 s\n",
      "第23轮训练loss：0.0012831930300593376\n",
      "第23轮训练精度：0.99986\n",
      "------\n",
      "第23轮测试用时：2.2941722869873047 s\n",
      "第23轮测试loss：0.6726627410888671\n",
      "第23轮测试精度：0.8675\n",
      "------\n",
      "第24轮训练用时：13.846631288528442 s\n",
      "第24轮训练loss：0.0013759332906547934\n",
      "第24轮训练精度：0.99988\n",
      "------\n",
      "第24轮测试用时：2.2907841205596924 s\n",
      "第24轮测试loss：0.6682148189544678\n",
      "第24轮测试精度：0.8675\n",
      "------\n",
      "第25轮训练用时：13.902048349380493 s\n",
      "第25轮训练loss：0.0011874874610826372\n",
      "第25轮训练精度：0.99984\n",
      "------\n",
      "第25轮测试用时：2.26128888130188 s\n",
      "第25轮测试loss：0.674615983581543\n",
      "第25轮测试精度：0.8682\n",
      "------\n",
      "第26轮训练用时：13.82737684249878 s\n",
      "第26轮训练loss：0.0013605149091780186\n",
      "第26轮训练精度：0.99984\n",
      "------\n",
      "第26轮测试用时：2.2840495109558105 s\n",
      "第26轮测试loss：0.6786968957901001\n",
      "第26轮测试精度：0.8675\n",
      "------\n",
      "第27轮训练用时：13.922944068908691 s\n",
      "第27轮训练loss：0.0011825808470789343\n",
      "第27轮训练精度：0.99988\n",
      "------\n",
      "第27轮测试用时：2.265937089920044 s\n",
      "第27轮测试loss：0.6762783555984497\n",
      "第27轮测试精度：0.8679\n",
      "------\n",
      "第28轮训练用时：13.846524000167847 s\n",
      "第28轮训练loss：0.0012209571540914477\n",
      "第28轮训练精度：0.99988\n",
      "------\n",
      "第28轮测试用时：2.403681755065918 s\n",
      "第28轮测试loss：0.6867093135833741\n",
      "第28轮测试精度：0.8678\n",
      "------\n",
      "第29轮训练用时：13.823259830474854 s\n",
      "第29轮训练loss：0.0011224987615924328\n",
      "第29轮训练精度：0.99988\n",
      "------\n",
      "第29轮测试用时：2.2944681644439697 s\n",
      "第29轮测试loss：0.6896695333480835\n",
      "第29轮测试精度：0.8661\n",
      "------\n",
      "第30轮训练用时：13.879491806030273 s\n",
      "第30轮训练loss：0.0010379337196424604\n",
      "第30轮训练精度：0.99992\n",
      "------\n",
      "第30轮测试用时：2.3267366886138916 s\n",
      "第30轮测试loss：0.684847723197937\n",
      "第30轮测试精度：0.869\n",
      "------\n",
      "第31轮训练用时：13.842999458312988 s\n",
      "第31轮训练loss：0.0011418969212286174\n",
      "第31轮训练精度：0.9999\n",
      "------\n",
      "第31轮测试用时：2.312248945236206 s\n",
      "第31轮测试loss：0.6804909608840942\n",
      "第31轮测试精度：0.8681\n",
      "------\n",
      "第32轮训练用时：13.867778301239014 s\n",
      "第32轮训练loss：0.001149166145399213\n",
      "第32轮训练精度：0.9999\n",
      "------\n",
      "第32轮测试用时：2.2755987644195557 s\n",
      "第32轮测试loss：0.6817935594558716\n",
      "第32轮测试精度：0.868\n",
      "------\n",
      "第33轮训练用时：13.849506855010986 s\n",
      "第33轮训练loss：0.0011571999596152454\n",
      "第33轮训练精度：0.99988\n",
      "------\n",
      "第33轮测试用时：2.2845630645751953 s\n",
      "第33轮测试loss：0.6743552482604981\n",
      "第33轮测试精度：0.8668\n",
      "------\n",
      "第34轮训练用时：13.868139743804932 s\n",
      "第34轮训练loss：0.001179021444041282\n",
      "第34轮训练精度：0.99986\n",
      "------\n",
      "第34轮测试用时：2.3124661445617676 s\n",
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      "------\n",
      "第35轮训练用时：13.875945568084717 s\n",
      "第35轮训练loss：0.0012564028087630869\n",
      "第35轮训练精度：0.9999\n",
      "------\n",
      "第35轮测试用时：2.2954604625701904 s\n",
      "第35轮测试loss：0.6786850824356079\n",
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      "------\n",
      "第36轮训练用时：13.866451501846313 s\n",
      "第36轮训练loss：0.001409976007277146\n",
      "第36轮训练精度：0.99978\n",
      "------\n",
      "第36轮测试用时：2.3016459941864014 s\n",
      "第36轮测试loss：0.680751223564148\n",
      "第36轮测试精度：0.8671\n",
      "------\n",
      "第37轮训练用时：13.827538251876831 s\n",
      "第37轮训练loss：0.0012660386971384287\n",
      "第37轮训练精度：0.99982\n",
      "------\n",
      "第37轮测试用时：2.275059223175049 s\n",
      "第37轮测试loss：0.6772991415023804\n",
      "第37轮测试精度：0.8684\n",
      "------\n",
      "第38轮训练用时：13.909807920455933 s\n",
      "第38轮训练loss：0.001109737637732178\n",
      "第38轮训练精度：0.99988\n",
      "------\n",
      "第38轮测试用时：2.2778303623199463 s\n",
      "第38轮测试loss：0.6813189832687377\n",
      "第38轮测试精度：0.8679\n",
      "------\n",
      "第39轮训练用时：13.843225002288818 s\n",
      "第39轮训练loss：0.001313181817289442\n",
      "第39轮训练精度：0.99984\n",
      "------\n",
      "第39轮测试用时：2.276646137237549 s\n",
      "第39轮测试loss：0.6844009311676026\n",
      "第39轮测试精度：0.8685\n",
      "------\n",
      "第40轮训练用时：13.871825456619263 s\n",
      "第40轮训练loss：0.001268579159406945\n",
      "第40轮训练精度：0.9999\n",
      "------\n",
      "第40轮测试用时：2.264920711517334 s\n",
      "第40轮测试loss：0.6890281217575073\n",
      "第40轮测试精度：0.8677\n",
      "------\n",
      "第41轮训练用时：13.832226037979126 s\n",
      "第41轮训练loss：0.0011534906955063342\n",
      "第41轮训练精度：0.99984\n",
      "------\n",
      "第41轮测试用时：2.3214027881622314 s\n",
      "第41轮测试loss：0.6775057241439819\n",
      "第41轮测试精度：0.8687\n",
      "------\n",
      "第42轮训练用时：13.8951416015625 s\n",
      "第42轮训练loss：0.0011820061339996755\n",
      "第42轮训练精度：0.99988\n",
      "------\n",
      "第42轮测试用时：2.272615671157837 s\n",
      "第42轮测试loss：0.6812481344223023\n",
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      "------\n",
      "第43轮训练用时：13.847173690795898 s\n",
      "第43轮训练loss：0.0011689054820500314\n",
      "第43轮训练精度：0.99988\n",
      "------\n",
      "第43轮测试用时：2.272902011871338 s\n",
      "第43轮测试loss：0.675494162940979\n",
      "第43轮测试精度：0.868\n",
      "------\n",
      "第44轮训练用时：13.858183860778809 s\n",
      "第44轮训练loss：0.0011440084389597177\n",
      "第44轮训练精度：0.99986\n",
      "------\n",
      "第44轮测试用时：2.297623634338379 s\n",
      "第44轮测试loss：0.6855692129135131\n",
      "第44轮测试精度：0.8683\n",
      "------\n",
      "第45轮训练用时：13.84883975982666 s\n",
      "第45轮训练loss：0.0011789536168985068\n",
      "第45轮训练精度：0.99984\n",
      "------\n",
      "第45轮测试用时：2.270059823989868 s\n",
      "第45轮测试loss：0.6808105405807495\n",
      "第45轮测试精度：0.8683\n",
      "------\n",
      "第46轮训练用时：13.884877443313599 s\n",
      "第46轮训练loss：0.0012568583230674268\n",
      "第46轮训练精度：0.9999\n",
      "------\n",
      "第46轮测试用时：2.2874555587768555 s\n",
      "第46轮测试loss：0.6774180152893067\n",
      "第46轮测试精度：0.8683\n",
      "------\n",
      "第47轮训练用时：13.826218366622925 s\n",
      "第47轮训练loss：0.0011540240642987191\n",
      "第47轮训练精度：0.99988\n",
      "------\n",
      "第47轮测试用时：2.308018684387207 s\n",
      "第47轮测试loss：0.6908522451400757\n",
      "第47轮测试精度：0.8664\n",
      "------\n",
      "第48轮训练用时：13.9056715965271 s\n",
      "第48轮训练loss：0.0010932112957537175\n",
      "第48轮训练精度：0.9999\n",
      "------\n",
      "第48轮测试用时：2.272169589996338 s\n",
      "第48轮测试loss：0.6848066528320312\n",
      "第48轮测试精度：0.8683\n",
      "------\n",
      "第49轮训练用时：13.831926345825195 s\n",
      "第49轮训练loss：0.00116355788866058\n",
      "第49轮训练精度：0.9999\n",
      "------\n",
      "第49轮测试用时：2.291761875152588 s\n",
      "第49轮测试loss：0.6827157232284546\n",
      "第49轮测试精度：0.8677\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 保存训练数据绘图\n",
    "train_loss_list = []\n",
    "train_accuracy_list = []\n",
    "\n",
    "test_loss_list = []\n",
    "test_accuracy_list = []\n",
    "\n",
    "epoch_step = []\n",
    "print(\"start train.\")\n",
    "for i in range(epoch):\n",
    "    epoch_step.append(i)\n",
    "    \n",
    "    mymod.train()\n",
    "    start_time = time.time()\n",
    "    total_train_loss_pre_epoch = 0\n",
    "    total_train_accuracy_pre_epoch = 0\n",
    "    for data in train_dataloader:\n",
    "        imgs, targets = data\n",
    "        # print(imgs.shape)\n",
    "        imgs = imgs.to(device)\n",
    "        targets = targets.to(device)\n",
    "        outputs = mymod(imgs)\n",
    "        accuracy = (outputs.argmax(1) == targets).sum()\n",
    "        total_train_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "        loss = loss_fn(outputs, targets)\n",
    "        total_train_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    end_train_time = time.time()\n",
    "    print(\"第{}轮训练用时：{} s\".format(i, end_train_time - start_time))\n",
    "    print(\"第{}轮训练loss：{}\".format(i, total_train_loss_pre_epoch / train_data_size))\n",
    "    print(\"第{}轮训练精度：{}\".format(i, total_train_accuracy_pre_epoch / train_data_size))\n",
    "    print(\"------\")\n",
    "    train_loss_list.append(total_train_loss_pre_epoch / train_data_size)\n",
    "    train_accuracy_list.append(total_train_accuracy_pre_epoch / train_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"train_loss\", total_train_loss_pre_epoch / train_data_size, i)\n",
    "    writer.add_scalar(\"train_acc\", total_train_accuracy_pre_epoch / train_data_size, i)\n",
    "    # 更新学习率\n",
    "    scheduler.step()\n",
    "    mymod.eval()\n",
    "    total_test_loss_pre_epoch = 0\n",
    "    total_test_accuracy_pre_epoch = 0\n",
    "    # 去掉梯度\n",
    "    with torch.no_grad():\n",
    "        for data in test_dataloader:\n",
    "            imgs, targets = data\n",
    "            imgs = imgs.to(device)\n",
    "            targets = targets.to(device)\n",
    "            outputs = mymod(imgs)\n",
    "\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            total_test_loss_pre_epoch += loss.item() * targets.size(0)\n",
    "            # 统计在测试集上正确的个数，然后累加起来\n",
    "            accuracy = (outputs.argmax(1) == targets).sum()\n",
    "            total_test_accuracy_pre_epoch += accuracy.item()\n",
    "\n",
    "\n",
    "    end_test_time = time.time()\n",
    "    print(\"第{}轮测试用时：{} s\".format(i, end_test_time - end_train_time))\n",
    "    print(\"第{}轮测试loss：{}\".format(i, total_test_loss_pre_epoch / test_data_size))\n",
    "    print(\"第{}轮测试精度：{}\".format(i, total_test_accuracy_pre_epoch / test_data_size))\n",
    "    print(\"------\")\n",
    "    test_loss_list.append(total_test_loss_pre_epoch / test_data_size)\n",
    "    test_accuracy_list.append(total_test_accuracy_pre_epoch / test_data_size)\n",
    "    \n",
    "    writer.add_scalar(\"test_loss\", total_test_loss_pre_epoch / test_data_size, i)\n",
    "    writer.add_scalar(\"test_acc\", total_test_accuracy_pre_epoch / test_data_size, i)\n",
    "\n",
    "\n",
    "torch.save(mymod, \"./{}.pth\".format(save_model_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e02c4d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-12T10:08:41.943245Z",
     "iopub.status.busy": "2024-05-12T10:08:41.942915Z",
     "iopub.status.idle": "2024-05-12T10:08:43.534374Z",
     "shell.execute_reply": "2024-05-12T10:08:43.533539Z"
    },
    "papermill": {
     "duration": 1.610381,
     "end_time": "2024-05-12T10:08:43.536461",
     "exception": false,
     "start_time": "2024-05-12T10:08:41.926080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gh82qDKdNbpc9/Nlpl9tpU4Yr/HWG0y63q/1/I+KBcAN0kNffpI17wn/sI3/8N+ypk9ff/v9onTareuWlqzTfrdL88IXilHy3euW5dUqBW9lpDklSYyCoLw426IuDXu2saWj+ukE7D3r1xcEG7av3KUZ/+9rJrodXvS1JynTZwxfc5ott5IIrqTnE+FVdFw40/mCoQ6/isluV4bLrePnPMCSvP6iGE2xNyHTZZbdZmkNMUKETrFOb1aKeuWkqzQt/X0sjH3nh73V+hlNfHGzQiu0HtWLbQS3fflCf7K5TsNULW6Lf81hx2qwqzHSqW5br8EemSxkuuzbtrdcne2r16Z76o37PDEPaWdOgnTUNWvL5/lbPF2a6VJDhlN0WDlVOm1UOe/jr6GObRb6mUDTE7K3t+M9IfNil5UsSXYi4s1stKs13q09hhvoUZqisMEN9CzOUn+HU9gNeban2RMPMlmqP9tX5OvwaZ/fK0b8mXxCH0rcP4QYpJ/Jf4Nb9Hm0/4FWNN6CmkKFgKBT+HAz/Z950xOPgEf91hT+a/ysLKfrY62/Sxqo6ban2tBkoXHarTivKlNNmDf/X3xS+UPojXwdCamwKRv9b+7z5j0dbctIdctgsqq73H/f9WixSvtupgszDrTKFzZ/zM5zKSrOrtrFJ+49o1amu92m/x6/99T7VNATafD8WS/hCGL44WWW3WlTnbVRDMJw4Ii0jO2vad+HNSrNHL6SZLru8/mD0HHWNTar3BdQYCF/gwnV3/Pf+ZU67tUULTeQjZBhHvM7hFp2m5jBR72s66jkdNku05c1uszT/nBzxX7vR8r92f1P452rHgQbtONCg9ze3DgBOm7XNi3lxdprO652r807J09klWVq97H0NHjZCHr+hmga/DnkDqmkIqMYb0KGGgGq8fnn9QRmKlEctf36bf4Ybm4KqrvOptrFJ/mBIuw41atehxmPWZabLrjN7ZGlAj2wN6JmtM3tk6/SiLHl8Tdq636tt+z3a1vx5636vth/wRn+2qus7fjGUpPwMp7pnudQ9O01FWS7lpDvkD4bU0BxgGwPhz+HHITUGwr9fLoc13FrgtCnDFf7sbm5RyHCFW3l8TSHVNgRU29jU/Dmg2oYm1TWGt9X7mmSVobyM5tbMDKfy3I5oy2Z+hlO5bqcyoy1lFlmtbbeWhUJqPm9AddHX+9Lr+ppkt1rkiIRAu7U5AB4OhXZb+NyxcKghEA4t+z1qDIS0pTm4tFdBhlN9CjNUmu9WMGTI62+SxxduFfP4g/L6muQNBOX1hVs73U5bTMrdWYQbJK3ahoA210rzV+zUzhqfth7xx7a28egXq1gozHQ1/8Fv/uPfI1t9CjNkP04zrGGEm5331vq044BXOw6GLwrbDzRo+wGvvjjg1X6PX4caAtFjMpw29cpzq1deunrlpaskLz36uEdOuvIznLKdQBdXUzCkmoaALJIc9sh/1tZW5wwEAnr11Vc15uuXq6FJzRfZcFkPNV9wD3rDgSQSYrpluVTY/DnNcfw/doFgSJ7m8OHxBWXo+E0oGc7OdWcZRrjrKRJ4moKhaDdHpEvRabN2uPswFDK0r96n7Qe82nEg8v316ovm7/Ge2kb5gyHZrRYN7Jmt83rn6bxT8jSkd5565qYfrotAQLvXSueW5srhcHSoDEfTGAhqv8cfbllr/qhu7i481BBQWWFG9Oe5V156m+89zWFTQaZLQ3rntXqutjGg7fu9qm0IHO7WjHRzNoe+yNcOm1VF2S51y0pr/uySy564C2JDo08LFizQN8deErP67opCIUNVdY3asi/8z9XW5pCzZb9HBzz+cHdjQUa0VSfSshNplW0Pf3MXbiIRbpCU9hxq1KUz3pHHZ5c+/rjNfYqyXepdkKFumS7ZbRbZrBbZrRbZrNbmz5boZ6vVIltb4xasLcct9OueqTN7ZKl7Vlqnym2xhFsBIt0Uban3NWnHAa+CIUO98tKVk+447hiJE2G3WVWY6Wr3/jarRXkZDuVlOCVlxLQsDptVue7wf8jxZrFYlNY8bqNbVvvf//FYrRYVZaepKDtN55flt3q+MRBUVW2jirLT2hX4YinNYVNJbrpKjghRsZSd5tBZJTlxOXe82W3W43aDpgKr1aIeOeF/jEb0K4zLazjt1g6Pm4s1wg2S0r9W75THF1S6zdC5ZQXqU5ipsoIMnVIQ/q/jlHy30hPcLNpZ4e6A7EQXA3GS5rCpd0FsQyGAlgg3SEqvrN0jSRp7SkjTvzM0pZuRAQAdwzo3SDpfHPRq9Y4aWSzS4HxTpwoBAJIA4QZJZ8G6cKvN+b3zlB3/oRkAgCRDuEHSeWXtbknS5WcVJbgkAICuiHCDpLKrpkErt4e7pEYPINwAAFoj3CCpvNrcanN+73x1j+H0XQBA6iDcIKlEws03BhUnuCQAgK6KcIOksaumQSuau6QuH9Qj0cUBAHRRhBskjdeaZ0kN7Z2nouzOrRAMAEh9hBskjdeiXVK02gAAjo5wg6Sw51CjPtp2UJJ0+VmEGwDA0RFukBReWxdutRnSO0/FOXRJAQCOjnCDpPAqXVIAgHYi3KDLq6o93CXFFHAAwPEQbtDlvbZ2twxDOu+UXPXISU90cQAAXRzhBl3eq81TwOmSAgC0B+EGXdre2kZ9uPWAJMINAKB9CDfo0hZ8vEeGIZ17Sq565tIlBQA4PsINurRX1jTPkmJtGwBAOxFu0GXtrWvUsuYuqcuZJQUAaCfCDbqs19eFu6QGl+aqV5470cUBACQJwg26rFeaF+4bS6sNAKADCDfokvbV+bRsS3OXFONtAAAdQLhBl/T6x3sUMqTBvXJUmk+XFACg/Qg36JK4lxQAoLMIN+hyqut9+uDz/ZIINwCAjiPcoMt585O9ChnSoBK6pAAAHUe4QZcTabW5pH+3BJcEAJCMCDfocpY2z5Ia1ic/wSUBACQjwg26lC8OerWzpkE2q0XnnZKX6OIAAJIQ4QZdSuQO4GeV5CjDZU9waQAAyYhwgy4lsnDfcLqkAACdRLhBl7KUcAMAOEGEG3QZ++p8+nyfRxaLNLQ34QYA0DmEG3QZkfE2ZxRnK8ftSHBpAADJinCDLoPxNgCAWCDcoMuILN7H+jYAgBNBuEGXUOP1a2NVnSTp/DLCDQCg8wg36BI+2npQhiH17ZahblmuRBcHAJDECDfoEpZtZbwNACA2CDfoErifFAAgVhIebmbOnKmysjKlpaVp+PDhWrZs2TH3f/zxx9W/f3+lp6ertLRUP/nJT9TY2GhSaREPHl+T1u08JEka1qcgwaUBACS7hIabuXPnqqKiQlOnTtWKFSs0ePBgjRkzRnv37m1z/7/97W+6++67NXXqVH3yySd66qmnNHfuXP385z83ueSIpRXbDyoYMlSSm66S3PREFwcAkOQSemfCGTNm6LbbbtPEiRMlSU888YReeeUVzZo1S3fffXer/d9//32NHDlSN910kySprKxM3/rWt7R06dKjvobP55PP54s+rq2tlSQFAgEFAoFYvp3o+WJ93lS3ZFO1JOn83rkdqjvq21zUt7mob3NR3+bqTH13ZN+EhRu/36/ly5drypQp0W1Wq1WjRo3SkiVL2jxmxIgRmj17tpYtW6Zhw4bp888/16uvvqqbb775qK8zffp0TZs2rdX2hQsXyu12n/gbaUNlZWVczpuqXl9nk2RRWt0XevXVHR0+nvo2F/VtLurbXNS3uTpS316vt937JizcVFdXKxgMqqioqMX2oqIibdiwoc1jbrrpJlVXV+uCCy6QYRhqamrS97///WN2S02ZMkUVFRXRx7W1tSotLdXo0aOVnZ0dmzfTLBAIqLKyUpdddpkcDm4f0B6+QFB3ffiWpJC+d+VFKivIaPex1Le5qG9zUd/mor7N1Zn6jvS8tEdCu6U6avHixXr44Yf1hz/8QcOHD9emTZt055136qGHHtJ9993X5jEul0suV+t1UxwOR9x+gON57lSz8os6+ZtC6pblUr+iHFkslg6fg/o2F/VtLurbXNS3uTpS3x35viQs3BQWFspms6mqqqrF9qqqKhUXF7d5zH333aebb75Zt956qyRp0KBB8ng8uv3223XPPffIak345C900LIth2+50JlgAwDAlyUsDTidTg0ZMkSLFi2KbguFQlq0aJHKy8vbPMbr9bYKMDabTZJkGEb8Cou4WcrNMgEAMZbQbqmKigpNmDBBQ4cO1bBhw/T444/L4/FEZ0/dcsstKikp0fTp0yVJV1xxhWbMmKFzzz032i1133336YorroiGHCSPpmBIy7cdlMTifQCA2ElouLnhhhu0b98+3X///dqzZ4/OOeccLViwIDrIePv27S1aau69915ZLBbde++92rlzp7p166YrrrhCv/zlLxP1Frq8HQe8KspOk9Pe9brs1u2qldcfVE66Q6d3z0p0cQAAKSLhA4onT56syZMnt/nc4sWLWzy22+2aOnWqpk6dakLJkt/rH+/R//vrct1+UV/9/BtnJro4rUTG25xfli+rlfE2AIDY6Hr/ziNmnn1/qyTpzQ1tr/gcDx0Z+7SM8TYAgDgg3KSoXTUNWvJ5uGVk87561fua4v6a9764VkN/8YZWbD943H1DISMabhhvAwCIJcJNinpx1U5FGlEMQ/q4+caU8bJpb51mf7Bd+z1+3fbsR9q233PM/TdW1am2sUlup00De8Z2MUUAwMmNcJOCDMPQCyt2SpLSHOFv8do4h5s/Lv48+vV+j18Tn/5QNV7/UfePtNoM6Z0nu40fQwBA7HBVSUEf76rVZ3vr5bJbNaG8TFJ8w83Omga9tCocpv508xD1zEnT59Ue3f5/y+VrCrZ5TCTcfKVvQdzKBQA4ORFuUtA/VnwhSRo1oEgj+hVKktZ+Eb9w8+d3PldTyNDIfgUaPbBYsyaeryyXXcu2HtBd89YoFGo5yNgwjOjifYy3AQDEGuEmxQSCIf179S5J0rXnlWhQSY4k6fNqj2ob23+7+Paqrvfp+WXbJUl3XNJPknRGcbb++O0hslst+tfqXZpR+WmLY7ZUe1Rd75PTbtXZvXJiXiYAwMmNcJNi3v1sn6rr/SrIcOrC07opP8OpXnnpkqR1ceiaevo/W+RrCmlwrxyNOPVwF9MFpxXq4WsGSZL+961Nmvvh9uhzkS6pc0tz5bKzsjQAILYINynmn80Dia88p6cczQN1I60jse6aqmsM6P+WbJMk/eCSfq1ufHn9+aX64dfCrTk/f2Gd3vl0nyTWtwEAxBfhJoXUNga0cH34Luvjzu0V3T6oJFeStCbGLTezP9iuusYm9eueqdEDitrcp+Ky03XVOT0VDBm647kV2rCn9ojxNgwmBgDEHuEmhby2drf8TSGd1j1TZ5UcXjsmHi03jYGgnnpviyTpBxefetTbJ1gsFj3yX2drWJ981fua9O2/LNXOmgbZrRad1zs3ZuUBACCCcJNC/tHcJXXNeSUtuojO6hkON9sPeI+59kxHzPtoh6rrfSrJTdeV5/Q85r4uu01/unmI+nbLUHV9+PXPKsmR25nwW5sBAFIQ4SZF7Djg1bItB2SxSFefU9LiuRy3Q2UFbkmxWe+mKRjSk++EF+27/aK+0bE9x5LrdurZicNUmOmUxHgbAED8EG5SRGQRvfK+BeqZm97q+UG9ciVJa2LQNfXvNbv0xcEGFWY6dcP5pe0+rjTfrdm3Dtd3RpTpexf2OeFyAADQFsJNCjAMIzpLatx5vdrc5+yS2Iy7CYUM/XHxZknSxJF9lObo2FTuM4qz9cCVA9U9K+2EygEAwNEQblLA6i8O6fNqj9IcVn39rOI29zkrEm5OsFvqjU+q9GlVvbJcdt1c3vuEzgUAQDwQblLAP5tvt/D1gcXKdLU9SDcye2pnTYOq632deh3DMPSH5labb5f3Vnaao1PnAQAgngg3Sc7fdPh2C9ccpUtKkrLSHOrbLUNS51tvlny+X6t21Mhlt+q7IxkzAwDomgg3SW7xxr066A2oe5ZLI0899qJ4JzruJjLW5obzS9Uty9WpcwAAEG+EmyT3wsrwQOKrzukp+3GmZJ/IjKk1X9To3c+qZbNadNuFfTt8PAAAZiHcJLFD3oAWfbJX0tFnSR0pulLxzpoOv9Yf3gq32lw1uKdK890dPh4AALMQbpLYy2t3yR8M6YziLJ3ZI/u4+w/okS2rRaqq9amqtrHdr7OzpkGvr98jSfr+Jad2urwAAJiBcJPEXoiubVNynD3DMlx29eueKalj427mfbRDhiGNOLVApxdldbygAACYiHCTpLbt9+ijbQdltUhXndO+cCN1/A7hwZCheR+Fp5p3ZDViAAAShXCTpF5aFZ7+PbJfoYqy27/a7+E7hNe0a///bKrWzpoGZafZNWZg2wsEAgDQlRBuktTqHTWSpMsGFHXouEHRQcW1MgzjuPvP/WiHJOmac0s6fKsFAAASgXCTpLbs90iS+hZmdui4AT2yZbNaVF3v057jDCo+4PFr4cfhgcTX0yUFAEgShJsk1BQMaccBrySprLBj07LTHLbooODjrXfzwsqdCgQNnVWSrYE9czpXWAAATEa4SUK7ahoVCBpy2q3qmZPe4ePbs1KxYRj6+4fhLqkbzj+lcwUFACABCDdJKNIl1TvfLavV0uHjI+NujjVjatWOGm2sqpPLbtWVg3t2rqAAACQA4SYJba0Oh5s+hRmdOv7IGVNHG1T89+aBxN8Y1EM56dz9GwCQPAg3SWjLCYab/sVZctgsOugN6IuDDa2e9/ia9K/mqeasbQMASDaEmyQUCTdlnQw3LrtNZxSHb9ewto2uqVfW7pbHH1RZgVvD++R3vqAAACQA4SYJbW0ec1NW0LlwIx0x7qaNQcWRgcTXn18qi6XjY3oAAEgkwk2SCQRD0a6kznZLSUfMmPrSHcI37a3TR9sOyma16L/acadxAAC6GsJNktlxwKtgyFC6w6aibFenz3Nky82Rg4r/3nwfqa/276buHbitAwAAXQXhJslEuqR6F7hPqMvo9KIsOe1W1TU2adv+8IKA/qaQ/rkicpNM1rYBACQnwk2S2VIdDiIn0iUlSQ6bVQN6hAcVR9a7eXNDlarr/eqW5dJX+3c7sYICAJAghJsks/UEZ0odaVBJyzuEz20eSHzteb1kt/GjAQBITlzBkkykW6rPCcyUijhy3M3uQw16+9N9kljbBgCQ3Ag3SSa6gF+3Ew83kZWK1+08pL9/+IVChjSsT/4Jd3kBAJBIhJsk4msKamdNeBr4iaxxE9GvW6bSHFZ5/EH95d3PJUk3DKXVBgCQ3Ag3SWT7fq8MQ8p02VWY6Tzh89ltVg3sGW69qfM1Kctl1zcG9Tjh8wIAkEiEmyRy+LYLJzYN/EiRQcWSdOU5PZXutMXkvAAAJArhJonE4rYLXxYZdyMxkBgAkBrsiS4A2i9Wa9wcacSphcp02TWoJKdFKw4AAMmKcJNEomvcxLDlpjgnTe9P+ZqcNis3yQQApATCTRKJdkvFeKp2dpojpucDACCRGHOTJBr8Qe0+1Cgptt1SAACkGsJNkth2INxqk5PuUJ6blhYAAI6GcJMkjrynFGNjAAA4OsJNkojOlCpwJ7gkAAB0bYSbJBHLu4EDAJDKCDdJInrDTMINAADHRLhJElvisDoxAACpiHCTBOp9TdpX55NEtxQAAMdDuEkCkfE2+RlO5aQzDRwAgGMh3CSBwzfMZKYUAADHQ7hJAsyUAgCg/Qg3SSCyxk1fwg0AAMfVJcLNzJkzVVZWprS0NA0fPlzLli076r6XXHKJLBZLq4+xY8eaWGJzxeuGmQAApKKEh5u5c+eqoqJCU6dO1YoVKzR48GCNGTNGe/fubXP/f/7zn9q9e3f0Y926dbLZbLruuutMLrl5ot1STAMHAOC47IkuwIwZM3Tbbbdp4sSJkqQnnnhCr7zyimbNmqW777671f75+fktHs+ZM0dut/uo4cbn88nn80Uf19bWSpICgYACgUCs3kb0nEd+joW6xoD2e/ySpJIcZ8zLnMziUd84OurbXNS3uahvc3Wmvjuyr8UwDKPDpYoRv98vt9ut+fPn6+qrr45unzBhgmpqavTSSy8d9xyDBg1SeXm5/vSnP7X5/AMPPKBp06a12v63v/1NbnfXn320vV76zVq7sh2GHhoaTHRxAABICK/Xq5tuukmHDh1Sdnb2MfdNaMtNdXW1gsGgioqKWmwvKirShg0bjnv8smXLtG7dOj311FNH3WfKlCmqqKiIPq6trVVpaalGjx593MrpqEAgoMrKSl122WVyOGKzHs2/Vu+W1q7V6T3z9I1vDIvJOVNFPOobR0d9m4v6Nhf1ba7O1Hek56U9Et4tdSKeeuopDRo0SMOGHf2i73K55HK5Wm13OBxx+wGO5bl31DRKkvp2y+QX7iji+b1Ea9S3uahvc1Hf5upIfXfk+5LQAcWFhYWy2Wyqqqpqsb2qqkrFxcXHPNbj8WjOnDn63ve+F88iJhxr3AAA0DEJDTdOp1NDhgzRokWLottCoZAWLVqk8vLyYx47b948+Xw+ffvb3453MRNqy/7wGjd9mCkFAEC7JLxbqqKiQhMmTNDQoUM1bNgwPf744/J4PNHZU7fccotKSko0ffr0Fsc99dRTuvrqq1VQUJCIYpuGlhsAADom4eHmhhtu0L59+3T//fdrz549Ouecc7RgwYLoIOPt27fLam3ZwLRx40a99957WrhwYSKKbJqDHr8ONYSnvrHGDQAA7ZPwcCNJkydP1uTJk9t8bvHixa229e/fXwmcwW6aLc0rE/fISVO605bg0gAAkBwSvkIxjo6ViQEA6DjCTRfGeBsAADqOcNOFRWdKFXb9lZQBAOgqCDddGN1SAAB0HOGmizIMIxpu+tAtBQBAuxFuuqjqer/qfE2yWKTSfLqlAABoL8JNF7W1eRp4z5x0pTmYBg4AQHsRbrqoLc1dUn270SUFAEBHEG66KAYTAwDQOYSbLirSLcUaNwAAdAzhpovaUs0aNwAAdAbhpgsyDEPb9tMtBQBAZxBuuqC9dT55/UHZrBamgQMA0EGEmy4oMlOqV166HDa+RQAAdARXzi6ImVIAAHQe4aYL2rKf2y4AANBZhJsu5qDHr1XbayRJZQWMtwEAoKPsiS4ApO37vVq4fo8q11fpo20HFQwZkqTTirISXDIAAJIP4SYBDMPQ2p2HVLm+Sgs/rtLGqroWz5/ZI1tXDO6hr/QtSFAJAQBIXoQbkz359mY9/Z+t2lPbGN1ms1o0rCxfowcWadSZRUz/BgDgBBBuTORrCup/FmxQyJDcTpsu6d9Nlw0o0lf7d1eu25no4gEAkBIINyby+IJqHk6j5fdepnSnLbEFAgAgBTFbykQeX5MkyWW3EmwAAIgTwo2JPP5wuMl00WAGAEC8EG5MFGm5ySDcAAAQN4QbE3l8QUnhwcQAACA+CDcmirTc0C0FAED8EG5M5PE3t9wQbgAAiBvCjYkOt9zQLQUAQLwQbkwUmS3ldtJyAwBAvBBuTMSYGwAA4o9wY6LIbKkMuqUAAIibToWba6+9Vv/zP//Tavsjjzyi66677oQLlaoiLTd0SwEAED+dCjfvvPOOvvGNb7Tafvnll+udd9454UKlKlYoBgAg/joVburr6+V0tr6LtcPhUG1t7QkXKlWxiB8AAPHXqXAzaNAgzZ07t9X2OXPmaMCAASdcqFTFgGIAAOKvU1fZ++67T+PGjdPmzZv1ta99TZK0aNEiPf/885o3b15MC5hKWMQPAID469RV9oorrtCLL76ohx9+WPPnz1d6errOPvtsvfHGG7r44otjXcaUwSJ+AADEX6ebEMaOHauxY8fGsiwpz8sifgAAxF2nxtx8+OGHWrp0aavtS5cu1UcffXTChUpV9Yy5AQAg7joVbiZNmqQdO3a02r5z505NmjTphAuVipqCITUGQpKkDMINAABx06lws379ep133nmttp977rlav379CRcqFXkDwejXTAUHACB+OhVuXC6XqqqqWm3fvXu37HZaJdoSGUxst1rksnPXCwAA4qVTV9nRo0drypQpOnToUHRbTU2Nfv7zn+uyyy6LWeFSyZEL+FkslgSXBgCA1NWpZpZf//rXuuiii9S7d2+de+65kqRVq1apqKhIf/3rX2NawFTBAn4AAJijU1fakpISrVmzRs8995xWr16t9PR0TZw4Ud/61rfkcDhiXcaUELmvFAv4AQAQX52+0mZkZOiCCy7QKaecIr/fL0l67bXXJElXXnllbEqXQiLdUsyUAgAgvjp1pf388891zTXXaO3atbJYLDIMo8U4kmAweIyjT05eP6sTAwBghk4NKL7zzjvVp08f7d27V263W+vWrdPbb7+toUOHavHixTEuYmqILODH6sQAAMRXp660S5Ys0ZtvvqnCwkJZrVbZbDZdcMEFmj59un70ox9p5cqVsS5n0mNAMQAA5uhUy00wGFRWVpYkqbCwULt27ZIk9e7dWxs3boxd6VLIkVPBAQBA/HSqGeGss87S6tWr1adPHw0fPlyPPPKInE6n/vSnP6lv376xLmNKoOUGAABzdOpKe++998rj8UiSHnzwQX3zm9/UhRdeqIKCAs2dOzemBUwVHn+k5YZwAwBAPHXqSjtmzJjo1/369dOGDRt04MAB5eXlsfruUURabjKYLQUAQFzFrBkhPz8/VqdKSZGp4KxzAwBAfHEHR5PU+wg3AACYgXBjkshsKRbxAwAgvgg3JoneW4oBxQAAxBXhxiRMBQcAwByEG5N4WcQPAABTJDzczJw5U2VlZUpLS9Pw4cO1bNmyY+5fU1OjSZMmqUePHnK5XDr99NP16quvmlTazjEMI9otRcsNAADxldAr7dy5c1VRUaEnnnhCw4cP1+OPP64xY8Zo48aN6t69e6v9/X6/LrvsMnXv3l3z589XSUmJtm3bptzcXPML3wGNgZBCRvhrN+EGAIC4SuiVdsaMGbrttts0ceJESdITTzyhV155RbNmzdLdd9/dav9Zs2bpwIEDev/99+VwOCRJZWVlZha5UyLTwCXJ7aBbCgCAeEpYuPH7/Vq+fLmmTJkS3Wa1WjVq1CgtWbKkzWP+9a9/qby8XJMmTdJLL72kbt266aabbtLPfvYz2Wxthwafzyefzxd9XFtbK0kKBAIKBAIxfEeKnu/L5z3kbZQUHm8TDDYpGIzpy560jlbfiA/q21zUt7mob3N1pr47sm/Cwk11dbWCwaCKiopabC8qKtKGDRvaPObzzz/Xm2++qfHjx+vVV1/Vpk2bdMcddygQCGjq1KltHjN9+nRNmzat1faFCxfK7Xaf+BtpQ2VlZYvHX3gkyS670dTlxwcloy/XN+KL+jYX9W0u6ttcHalvr9fb7n2TagBIKBRS9+7d9ac//Uk2m01DhgzRzp079eijjx413EyZMkUVFRXRx7W1tSotLdXo0aOVnZ0d0/IFAgFVVlbqsssui3abSdKHWw9Kaz5UfnaGvvGNC2L6miezo9U34oP6Nhf1bS7q21ydqe9Iz0t7JCzcFBYWymazqaqqqsX2qqoqFRcXt3lMjx495HA4WnRBnXnmmdqzZ4/8fr+cTmerY1wul1wuV6vtDocjbj/AXz63LxT+nOGy80sTB/H8XqI16ttc1Le5qG9zdaS+O/J9SdhUcKfTqSFDhmjRokXRbaFQSIsWLVJ5eXmbx4wcOVKbNm1SKBSKbvv000/Vo0ePNoNNV+HhvlIAAJgmoevcVFRU6M9//rOeffZZffLJJ/rBD34gj8cTnT11yy23tBhw/IMf/EAHDhzQnXfeqU8//VSvvPKKHn74YU2aNClRb6FdIgv4ZbCAHwAAcZfQpoQbbrhB+/bt0/333689e/bonHPO0YIFC6KDjLdv3y6r9XD+Ki0t1euvv66f/OQnOvvss1VSUqI777xTP/vZzxL1FtqFO4IDAGCehF9tJ0+erMmTJ7f53OLFi1ttKy8v1wcffBDnUsWWt3l14gxumgkAQNwl/PYLJ4P6SLcULTcAAMQd4cYE3uh9pRhzAwBAvBFuTBAZc8N9pQAAiD/CjQmYCg4AgHkINybw+pkKDgCAWQg3JmAqOAAA5iHcmODwIn6EGwAA4o1wY4LDLTd0SwEAEG+EGxNEF/GjWwoAgLgj3JjAwyJ+AACYhnATZ/6mkPzB8F3MMxlzAwBA3BFu4izSJSVJbsbcAAAQd4SbOIsMJnbarXLYqG4AAOKNq22csYAfAADmItzEGQv4AQBgLsJNnLGAHwAA5iLcxBkL+AEAYC7CTZyxgB8AAOYi3MSZJ9JyQ7cUAACmINzEWT2rEwMAYCrCTZwd7pZizA0AAGYg3MQZU8EBADAX4SbODk8Fp+UGAAAzEG7irJ7ZUgAAmIpwE2deZksBAGAqwk2ceZgtBQCAqQg3ceZhthQAAKYi3MSZh9lSAACYinATZ/XcOBMAAFMRbuKMRfwAADAX4SaOQiFDXj8DigEAMBPhJo68gWD0a7qlAAAwB+EmjiKDia0WKc1BVQMAYAauuHHkOWIBP4vFkuDSAABwciDcxBEL+AEAYD7CTRwdviM4M6UAADAL4SaOvNw0EwAA0xFu4qiem2YCAGA6wk0cHV7jhm4pAADMQriJI+4rBQCA+Qg3cRSZLeWmWwoAANMQbuLI0zygOJNuKQAATEO4iaNItxQtNwAAmIdwE0eRcJPJmBsAAExDuImjelYoBgDAdISbODq8iB9jbgAAMAvhJo48LOIHAIDpCDdx5GlexM9Nyw0AAKYh3MQRA4oBADAf4SaOmAoOAID5CDdxYhhGtFuKlhsAAMxDuIkTX1NIwZAhidlSAACYiXATJ5EuKYluKQAAzES4iZPITTPTHTbZrJYElwYAgJMH4SZOPCzgBwBAQhBu4iS6gB+DiQEAMBXhJk6iC/gx3gYAAFMRbuLk8AJ+dEsBAGAmwk2csIAfAACJQbiJE269AABAYnSJcDNz5kyVlZUpLS1Nw4cP17Jly4667zPPPCOLxdLiIy0tzcTStk9kzA2zpQAAMFfCw83cuXNVUVGhqVOnasWKFRo8eLDGjBmjvXv3HvWY7Oxs7d69O/qxbds2E0vcPnRLAQCQGAkPNzNmzNBtt92miRMnasCAAXriiSfkdrs1a9asox5jsVhUXFwc/SgqKjKxxO1DtxQAAImR0Cuv3+/X8uXLNWXKlOg2q9WqUaNGacmSJUc9rr6+Xr1791YoFNJ5552nhx9+WAMHDmxzX5/PJ5/PF31cW1srSQoEAgoEAjF6J4qeM/K5rjH8dZrdEvPXQdiR9Y34o77NRX2bi/o2V2fquyP7JjTcVFdXKxgMtmp5KSoq0oYNG9o8pn///po1a5bOPvtsHTp0SL/+9a81YsQIffzxx+rVq1er/adPn65p06a12r5w4UK53e7YvJEvqays1OZtVklWbd20Qa/WfxKX10FYZWVlootwUqG+zUV9m4v6NldH6tvr9bZ736TrMykvL1d5eXn08YgRI3TmmWfqySef1EMPPdRq/ylTpqiioiL6uLa2VqWlpRo9erSys7NjWrZAIKDKykpddtllmr9vjXRgv84/92x949ySmL4Owo6sb4fDkejipDzq21zUt7mob3N1pr4jPS/tkdBwU1hYKJvNpqqqqhbbq6qqVFxc3K5zOBwOnXvuudq0aVObz7tcLrlcrjaPi9cPsMPhkDcQkiTluF38osRZPL+XaI36Nhf1bS7q21wdqe+OfF8SOqDY6XRqyJAhWrRoUXRbKBTSokWLWrTOHEswGNTatWvVo0ePeBWzU7i3FAAAiZHwK29FRYUmTJigoUOHatiwYXr88cfl8Xg0ceJESdItt9yikpISTZ8+XZL04IMP6itf+Yr69eunmpoaPfroo9q2bZtuvfXWRL6NViJ3BWcqOAAA5kr4lfeGG27Qvn37dP/992vPnj0655xztGDBgugg4+3bt8tqPdzAdPDgQd12223as2eP8vLyNGTIEL3//vsaMGBAot5Cmzy+8CJ+TAUHAMBcXeLKO3nyZE2ePLnN5xYvXtzi8WOPPabHHnvMhFKdmMOL+LFCMQAAZkr4In6pqCkYkq8pPKCYlhsAAMxFuIkDb/N9pSTJzb2lAAAwFeEmDiI3zXTYLHLZCTcAAJiJcBMH3DQTAIDEIdzEQaTlhvE2AACYj3ATB15/ZAE/uqQAADAb4SYOImvc0C0FAID5CDdxEBlzQ7cUAADmI9zEQWTMDQv4AQBgPsJNHETuK0XLDQAA5iPcxIE3MuaGAcUAAJiOcBMHkW6pDFpuAAAwHeEmDqJTwZktBQCA6Qg3cVDvo+UGAIBEIdzEweGp4Iy5AQDAbISbOPD6WcQPAIBEIdzEAVPBAQBIHMJNHESngrOIHwAApiPcxAFTwQEASBzCTRx4oncFJ9wAAGA2wk2MGcbhAcUZzJYCAMB0hJsY84fCAUdiQDEAAIlAuImx5rHEslikdActNwAAmI1wE2ONzeEmw2mXxWJJbGEAADgJEW5izB8Kf2YaOAAAiUG4ibFIyw3jbQAASAzCTYz5guGuKDczpQAASAjCTYz5jhhzAwAAzEe4ibFouKFbCgCAhCDcxJiveUAx4QYAgMQg3MSYLzqgmDE3AAAkAuEmxqIDihlzAwBAQhBuYqyRMTcAACQU4SbG/NHZUnRLAQCQCISbGKPlBgCAxCLcxNjh2VK03AAAkAiEmxiLDChmET8AABKDcBNjLOIHAEBiEW5ijHADAEBiEW5ijEX8AABILMJNjEUGFLOIHwAAiUG4iSFfU0hBo3lAMd1SAAAkBOEmhrz+pujXLOIHAEBiEG5iyNM84MZlt8puo2oBAEgErsAxFGm5cdNqAwBAwhBuYijScsN4GwAAEodwE0Oe5rtmZtJyAwBAwhBuYsjja+6WouUGAICEIdzEkKd5zA0zpQAASBzCTQx5m7ulGFAMAEDiEG5iiAHFAAAkHuEmhuiWAgAg8Qg3MUTLDQAAiUe4iSHG3AAAkHiEmxiKTAWn5QYAgMQh3MRQpOWGMTcAACQO4SaGPNxbCgCAhCPcxFB984DiTLqlAABIGMJNDHFXcAAAEo9wE0NMBQcAIPG6RLiZOXOmysrKlJaWpuHDh2vZsmXtOm7OnDmyWCy6+uqr41vAdqLlBgCAxEt4uJk7d64qKio0depUrVixQoMHD9aYMWO0d+/eYx63detW/fSnP9WFF15oUkmPLRgy1BAISaLlBgCAREp4uJkxY4Zuu+02TZw4UQMGDNATTzwht9utWbNmHfWYYDCo8ePHa9q0aerbt6+JpT26SKuNJGXScgMAQMIktInB7/dr+fLlmjJlSnSb1WrVqFGjtGTJkqMe9+CDD6p79+763ve+p3ffffeYr+Hz+eTz+aKPa2trJUmBQECBQOAE38FhNZ5GSZJVhixGMKbnRtsidUxdm4P6Nhf1bS7q21ydqe+O7JvQcFNdXa1gMKiioqIW24uKirRhw4Y2j3nvvff01FNPadWqVe16jenTp2vatGmtti9cuFBut7vDZT6aqgZJsstlk954442YnRfHV1lZmeginFSob3NR3+aivs3Vkfr2er3t3jepBofU1dXp5ptv1p///GcVFha265gpU6aooqIi+ri2tlalpaUaPXq0srOzY1a2tTsPSauWymWTLrvsMjkcjpidG20LBAKqrKykvk1CfZuL+jYX9W2uztR3pOelPRIabgoLC2Wz2VRVVdVie1VVlYqLi1vtv3nzZm3dulVXXHFFdFsoFB7Ea7fbtXHjRp166qktjnG5XHK5XK3O5XA4YvoDHJJVGU6b0mxNMT83jo36Nhf1bS7q21zUt7k6Ut8d+b4kdECx0+nUkCFDtGjRoui2UCikRYsWqby8vNX+Z5xxhtauXatVq1ZFP6688kp99atf1apVq1RaWmpm8VsYWpavVfddqrsHBxNWBgAA0AW6pSoqKjRhwgQNHTpUw4YN0+OPPy6Px6OJEydKkm655RaVlJRo+vTpSktL01lnndXi+NzcXElqtT1RLJZElwAAgJNbwsPNDTfcoH379un+++/Xnj17dM4552jBggXRQcbbt2+X1ZrwGesAACBJJDzcSNLkyZM1efLkNp9bvHjxMY995plnYl8gAACQtGgSAQAAKYVwAwAAUgrhBgAApBTCDQAASCmEGwAAkFIINwAAIKUQbgAAQEoh3AAAgJRCuAEAACmFcAMAAFIK4QYAAKSULnFvKTMZhiFJqq2tjfm5A4GAvF6vamtr5XA4Yn5+tER9m4v6Nhf1bS7q21ydqe/IdTtyHT+Wky7c1NXVSZJKS0sTXBIAANBRdXV1ysnJOeY+FqM9ESiFhEIh7dq1S1lZWbJYLDE9d21trUpLS7Vjxw5lZ2fH9Nxojfo2F/VtLurbXNS3uTpT34ZhqK6uTj179pTVeuxRNSddy43ValWvXr3i+hrZ2dn8cpiI+jYX9W0u6ttc1Le5Olrfx2uxiWBAMQAASCmEGwAAkFIINzHkcrk0depUuVyuRBflpEB9m4v6Nhf1bS7q21zxru+TbkAxAABIbbTcAACAlEK4AQAAKYVwAwAAUgrhBgAApBTCTYzMnDlTZWVlSktL0/Dhw7Vs2bJEFyllvPPOO7riiivUs2dPWSwWvfjiiy2eNwxD999/v3r06KH09HSNGjVKn332WWIKm+SmT5+u888/X1lZWerevbuuvvpqbdy4scU+jY2NmjRpkgoKCpSZmalrr71WVVVVCSpxcvvjH/+os88+O7qQWXl5uV577bXo89R1fP3qV7+SxWLRj3/84+g26jx2HnjgAVkslhYfZ5xxRvT5eNY14SYG5s6dq4qKCk2dOlUrVqzQ4MGDNWbMGO3duzfRRUsJHo9HgwcP1syZM9t8/pFHHtHvfvc7PfHEE1q6dKkyMjI0ZswYNTY2mlzS5Pf2229r0qRJ+uCDD1RZWalAIKDRo0fL4/FE9/nJT36if//735o3b57efvtt7dq1S+PGjUtgqZNXr1699Ktf/UrLly/XRx99pK997Wu66qqr9PHHH0uiruPpww8/1JNPPqmzzz67xXbqPLYGDhyo3bt3Rz/ee++96HNxrWsDJ2zYsGHGpEmToo+DwaDRs2dPY/r06QksVWqSZLzwwgvRx6FQyCguLjYeffTR6LaamhrD5XIZzz//fAJKmFr27t1rSDLefvttwzDCdetwOIx58+ZF9/nkk08MScaSJUsSVcyUkpeXZ/zlL3+hruOorq7OOO2004zKykrj4osvNu68807DMPj5jrWpU6cagwcPbvO5eNc1LTcnyO/3a/ny5Ro1alR0m9Vq1ahRo7RkyZIEluzksGXLFu3Zs6dF/efk5Gj48OHUfwwcOnRIkpSfny9JWr58uQKBQIv6PuOMM3TKKadQ3ycoGAxqzpw58ng8Ki8vp67jaNKkSRo7dmyLupX4+Y6Hzz77TD179lTfvn01fvx4bd++XVL86/qku3FmrFVXVysYDKqoqKjF9qKiIm3YsCFBpTp57NmzR5LarP/Ic+icUCikH//4xxo5cqTOOussSeH6djqdys3NbbEv9d15a9euVXl5uRobG5WZmakXXnhBAwYM0KpVq6jrOJgzZ45WrFihDz/8sNVz/HzH1vDhw/XMM8+of//+2r17t6ZNm6YLL7xQ69ati3tdE24AtGnSpElat25diz5yxF7//v21atUqHTp0SPPnz9eECRP09ttvJ7pYKWnHjh268847VVlZqbS0tEQXJ+Vdfvnl0a/PPvtsDR8+XL1799bf//53paenx/W16ZY6QYWFhbLZbK1GeFdVVam4uDhBpTp5ROqY+o+tyZMn6+WXX9Zbb72lXr16RbcXFxfL7/erpqamxf7Ud+c5nU7169dPQ4YM0fTp0zV48GD99re/pa7jYPny5dq7d6/OO+882e122e12vf322/rd734nu92uoqIi6jyOcnNzdfrpp2vTpk1x//km3Jwgp9OpIUOGaNGiRdFtoVBIixYtUnl5eQJLdnLo06ePiouLW9R/bW2tli5dSv13gmEYmjx5sl544QW9+eab6tOnT4vnhwwZIofD0aK+N27cqO3bt1PfMRIKheTz+ajrOLj00ku1du1arVq1KvoxdOhQjR8/Pvo1dR4/9fX12rx5s3r06BH/n+8THpIMY86cOYbL5TKeeeYZY/369cbtt99u5ObmGnv27El00VJCXV2dsXLlSmPlypWGJGPGjBnGypUrjW3bthmGYRi/+tWvjNzcXOOll14y1qxZY1x11VVGnz59jIaGhgSXPPn84Ac/MHJycozFixcbu3fvjn54vd7oPt///veNU045xXjzzTeNjz76yCgvLzfKy8sTWOrkdffddxtvv/22sWXLFmPNmjXG3XffbVgsFmPhwoWGYVDXZjhytpRhUOex9N///d/G4sWLjS1bthj/+c9/jFGjRhmFhYXG3r17DcOIb10TbmLk97//vXHKKacYTqfTGDZsmPHBBx8kukgp46233jIktfqYMGGCYRjh6eD33XefUVRUZLhcLuPSSy81Nm7cmNhCJ6m26lmS8fTTT0f3aWhoMO644w4jLy/PcLvdxjXXXGPs3r07cYVOYt/97neN3r17G06n0+jWrZtx6aWXRoONYVDXZvhyuKHOY+eGG24wevToYTidTqOkpMS44YYbjE2bNkWfj2ddWwzDME68/QcAAKBrYMwNAABIKYQbAACQUgg3AAAgpRBuAABASiHcAACAlEK4AQAAKYVwAwAAUgrhBgAApBTCDQAASCmEGwBJ5zvf+Y6uvvrqRBcDQBdFuAEAACmFcAOgy5o/f74GDRqk9PR0FRQUaNSoUbrrrrv07LPP6qWXXpLFYpHFYtHixYslSTt27ND111+v3Nxc5efn66qrrtLWrVuj54u0+EybNk3dunVTdna2vv/978vv9yfmDQKIC3uiCwAAbdm9e7e+9a1v6ZFHHtE111yjuro6vfvuu7rlllu0fft21dbW6umnn5Yk5efnKxAIaMyYMSovL9e7774ru92uX/ziF/r617+uNWvWyOl0SpIWLVqktLQ0LV68WFu3btXEiRNVUFCgX/7yl4l8uwBiiHADoEvavXu3mpqaNG7cOPXu3VuSNGjQIElSenq6fD6fiouLo/vPnj1boVBIf/nLX2SxWCRJTz/9tHJzc7V48WKNHj1akuR0OjVr1iy53W4NHDhQDz74oO666y499NBDslppzAZSAb/JALqkwYMH69JLL9WgQYN03XXX6c9//rMOHjx41P1Xr16tTZs2KSsrS5mZmcrMzFR+fr4aGxu1efPmFud1u93Rx+Xl5aqvr9eOHTvi+n4AmIeWGwBdks1mU2Vlpd5//30tXLhQv//973XPPfdo6dKlbe5fX1+vIUOG6Lnnnmv1XLdu3eJdXABdCOEGQJdlsVg0cuRIjRw5Uvfff7969+6tF154QU6nU8FgsMW+5513nubOnavu3bsrOzv7qOdcvXq1GhoalJ6eLkn64IMPlJmZqdLS0ri+FwDmoVsKQJe0dOlSPfzww/roo4+0fft2/fOf/9S+fft05plnqqysTGvWrNHGjRtVXV2tQCCg8ePHq7CwUFdddZXeffddbdmyRYsXL9aPfvQjffHFF9Hz+v1+fe9739P69ev16quvaurUqZo8eTLjbYAUQssNgC4pOztb77zzjh5//HHV1taqd+/e+s1vfqPLL79cQ4cO1eLFizV06FDV19frrbfe0iWXXKJ33nlHP/vZzzRu3DjV1dWppKREl156aYuWnEsvvVSnnXaaLrroIvl8Pn3rW9/SAw88kLg3CiDmLIZhGIkuBACY4Tvf+Y5qamr04osvJrooAOKIdlgAAJBSCDcAACCl0C0FAABSCi03AAAgpRBuAABASiHcAACAlEK4AQAAKYVwAwAAUgrhBgAApBTCDQAASCmEGwAAkFL+Pw2X772E1nVPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plt.plot(x,y)\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list)\n",
    "plt.title(\"train_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_loss_list)\n",
    "plt.title(\"test_loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list)\n",
    "plt.title(\"train_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, test_accuracy_list)\n",
    "plt.title(\"test_accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_loss_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_loss_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"loss\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.grid(True)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epoch_step, train_accuracy_list, label=\"train\")\n",
    "plt.plot(epoch_step, test_accuracy_list, label=\"test\")\n",
    "plt.legend()\n",
    "plt.title(\"accuracy\")\n",
    "plt.xlabel(\"step\")\n",
    "plt.ylabel(\"acc\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
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